Bibliographic record
Abstract
This chapter discusses the nature of weed biocontrol, attempts at biological control of weeds in European extensive and intensive agriculture, the biological control of non-native weeds in Europe, the possible combination of biological control with other weed control strategies (this has often been facilitated by interaction between different European Weed Research Society (EWRS) working groups). It considers legislation, particularly that from the EU, which affects and regulates biological control releases in Europe. The three fundamental approaches to biological pest management are named classical, inundative and conservation. Classical weed biocontrol is increasingly being combined with other control techniques to provide successful weed control. Some recent examples include biocontrol and herbicide application for the control of Lythrum salicaria L. (purple loosestrife), biocontrol, herbicide and replantings in Persicaria perfoliata (L.) H. Gross (mile-a-minute weed) control in the USA and herbicides and other management techniques in Euphorbia esula L. (leafy spurge) control in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".